Overview

Dataset statistics

Number of variables11
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory976.6 KiB
Average record size in memory100.0 B

Variable types

Categorical4
Numeric4
Text3

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15246/F/1/datasetView.do

Alerts

대여일자 has constant value ""Constant
씠슜嫄댁닔 is highly overall correlated with 씠룞嫄곕━(M) and 1 other fieldsHigh correlation
씠룞嫄곕━(M) is highly overall correlated with 씠슜嫄댁닔 and 1 other fieldsHigh correlation
씠슜떆媛(遺 is highly overall correlated with 씠슜嫄댁닔 and 1 other fieldsHigh correlation
대여구분코드 is highly imbalanced (65.0%)Imbalance
씠룞嫄곕━(M) has 1099 (11.0%) zerosZeros

Reproduction

Analysis started2024-03-13 16:24:11.152501
Analysis finished2024-03-13 16:24:13.538261
Duration2.39 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대여일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2021-12-01
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-12-01
2nd row2021-12-01
3rd row2021-12-01
4th row2021-12-01
5th row2021-12-01

Common Values

ValueCountFrequency (%)
2021-12-01 10000
100.0%

Length

2024-03-14T01:24:13.802318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T01:24:13.867264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-12-01 10000
100.0%

대여소번호
Real number (ℝ)

Distinct850
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean627.5502
Minimum3
Maximum1193
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:24:13.945351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile147
Q1331
median609
Q3916
95-th percentile1157
Maximum1193
Range1190
Interquartile range (IQR)585

Descriptive statistics

Standard deviation328.57958
Coefficient of variation (CV)0.52359091
Kurtosis-1.2277359
Mean627.5502
Median Absolute Deviation (MAD)297
Skewness0.1416618
Sum6275502
Variance107964.54
MonotonicityNot monotonic
2024-03-14T01:24:14.062769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1153 31
 
0.3%
785 31
 
0.3%
770 29
 
0.3%
1158 29
 
0.3%
1124 29
 
0.3%
207 29
 
0.3%
1020 27
 
0.3%
1166 25
 
0.2%
703 25
 
0.2%
415 25
 
0.2%
Other values (840) 9720
97.2%
ValueCountFrequency (%)
3 1
 
< 0.1%
102 16
0.2%
103 14
0.1%
104 11
0.1%
105 7
0.1%
106 15
0.1%
107 14
0.1%
108 13
0.1%
109 10
0.1%
111 5
 
0.1%
ValueCountFrequency (%)
1193 12
0.1%
1192 20
0.2%
1191 18
0.2%
1190 11
0.1%
1188 4
 
< 0.1%
1187 13
0.1%
1186 14
0.1%
1185 15
0.1%
1184 24
0.2%
1183 12
0.1%

대여소
Text

Distinct850
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-14T01:24:14.294860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length25
Mean length14.8887
Min length4

Characters and Unicode

Total characters148887
Distinct characters442
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)0.1%

Sample

1st row543. 구의공원(테크노마트 앞)
2nd row229. 양평1 보행육교 앞
3rd row488.푸르메병원
4th row763. 목동11단지 아파트
5th row509. 이마트 버스정류소 옆
ValueCountFrequency (%)
앞 2861
 
9.4%
옆 522
 
1.7%
출구 380
 
1.3%
1번출구 347
 
1.1%
사거리 333
 
1.1%
2번출구 305
 
1.0%
4번출구 278
 
0.9%
뒤 270
 
0.9%
건너편 214
 
0.7%
입구 202
 
0.7%
Other values (1762) 24669
81.2%
2024-03-14T01:24:14.670260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
20531
 
13.8%
. 9999
 
6.7%
1 7511
 
5.0%
2 4318
 
2.9%
3 3616
 
2.4%
4 3558
 
2.4%
구 3454
 
2.3%
앞 3417
 
2.3%
0 3327
 
2.2%
5 3292
 
2.2%
Other values (432) 85864
57.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 77849
52.3%
Decimal Number 36863
24.8%
Space Separator 20531
 
13.8%
Other Punctuation 10087
 
6.8%
Uppercase Letter 1768
 
1.2%
Close Punctuation 834
 
0.6%
Open Punctuation 834
 
0.6%
Dash Punctuation 55
 
< 0.1%
Lowercase Letter 28
 
< 0.1%
Connector Punctuation 23
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 3454
 
4.4%
앞 3417
 
4.4%
역 2872
 
3.7%
번 2571
 
3.3%
출 2529
 
3.2%
동 2214
 
2.8%
교 1518
 
1.9%
리 1506
 
1.9%
사 1223
 
1.6%
아 1182
 
1.5%
Other values (392) 55363
71.1%
Uppercase Letter
ValueCountFrequency (%)
K 239
13.5%
S 227
12.8%
C 202
11.4%
B 144
 
8.1%
D 121
 
6.8%
M 98
 
5.5%
G 87
 
4.9%
T 83
 
4.7%
I 77
 
4.4%
L 77
 
4.4%
Other values (9) 413
23.4%
Decimal Number
ValueCountFrequency (%)
1 7511
20.4%
2 4318
11.7%
3 3616
9.8%
4 3558
9.7%
0 3327
9.0%
5 3292
8.9%
7 3245
8.8%
6 2964
 
8.0%
8 2565
 
7.0%
9 2467
 
6.7%
Other Punctuation
ValueCountFrequency (%)
. 9999
99.1%
, 75
 
0.7%
? 13
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
k 14
50.0%
t 14
50.0%
Space Separator
ValueCountFrequency (%)
20531
100.0%
Close Punctuation
ValueCountFrequency (%)
) 834
100.0%
Open Punctuation
ValueCountFrequency (%)
( 834
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 55
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 23
100.0%
Math Symbol
ValueCountFrequency (%)
~ 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 77849
52.3%
Common 69242
46.5%
Latin 1796
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 3454
 
4.4%
앞 3417
 
4.4%
역 2872
 
3.7%
번 2571
 
3.3%
출 2529
 
3.2%
동 2214
 
2.8%
교 1518
 
1.9%
리 1506
 
1.9%
사 1223
 
1.6%
아 1182
 
1.5%
Other values (392) 55363
71.1%
Latin
ValueCountFrequency (%)
K 239
13.3%
S 227
12.6%
C 202
11.2%
B 144
 
8.0%
D 121
 
6.7%
M 98
 
5.5%
G 87
 
4.8%
T 83
 
4.6%
I 77
 
4.3%
L 77
 
4.3%
Other values (11) 441
24.6%
Common
ValueCountFrequency (%)
20531
29.7%
. 9999
14.4%
1 7511
 
10.8%
2 4318
 
6.2%
3 3616
 
5.2%
4 3558
 
5.1%
0 3327
 
4.8%
5 3292
 
4.8%
7 3245
 
4.7%
6 2964
 
4.3%
Other values (9) 6881
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 77849
52.3%
ASCII 71038
47.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
20531
28.9%
. 9999
14.1%
1 7511
 
10.6%
2 4318
 
6.1%
3 3616
 
5.1%
4 3558
 
5.0%
0 3327
 
4.7%
5 3292
 
4.6%
7 3245
 
4.6%
6 2964
 
4.2%
Other values (30) 8677
12.2%
Hangul
ValueCountFrequency (%)
구 3454
 
4.4%
앞 3417
 
4.4%
역 2872
 
3.7%
번 2571
 
3.3%
출 2529
 
3.2%
동 2214
 
2.8%
교 1518
 
1.9%
리 1506
 
1.9%
사 1223
 
1.6%
아 1182
 
1.5%
Other values (392) 55363
71.1%

대여구분코드
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
정기
8375 
일일(회원)
1528 
일일(비회원)
 
78
단체
 
19

Length

Max length7
Median length2
Mean length2.6502
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row정기
2nd row정기
3rd row정기
4th row정기
5th row정기

Common Values

ValueCountFrequency (%)
정기 8375
83.8%
일일(회원) 1528
 
15.3%
일일(비회원) 78
 
0.8%
단체 19
 
0.2%

Length

2024-03-14T01:24:14.774937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T01:24:14.846946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기 8375
83.8%
일일(회원 1528
 
15.3%
일일(비회원 78
 
0.8%
단체 19
 
0.2%

성별
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
M
3779 
\N
3052 
F
2512 
<NA>
655 
m
 
2

Length

Max length4
Median length1
Mean length1.5017
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row\N
2nd rowF
3rd row\N
4th row\N
5th row<NA>

Common Values

ValueCountFrequency (%)
M 3779
37.8%
\N 3052
30.5%
F 2512
25.1%
<NA> 655
 
6.6%
m 2
 
< 0.1%

Length

2024-03-14T01:24:14.935880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T01:24:15.017733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 3781
37.8%
n 3052
30.5%
f 2512
25.1%
na 655
 
6.6%

연령대코드
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
AGE_002
2599 
AGE_003
1972 
AGE_004
1552 
AGE_008
1239 
AGE_005
1215 
Other values (3)
1423 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAGE_005
2nd rowAGE_008
3rd rowAGE_008
4th rowAGE_004
5th rowAGE_004

Common Values

ValueCountFrequency (%)
AGE_002 2599
26.0%
AGE_003 1972
19.7%
AGE_004 1552
15.5%
AGE_008 1239
12.4%
AGE_005 1215
12.2%
AGE_001 830
 
8.3%
AGE_006 519
 
5.2%
AGE_007 74
 
0.7%

Length

2024-03-14T01:24:15.101975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T01:24:15.210124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
age_002 2599
26.0%
age_003 1972
19.7%
age_004 1552
15.5%
age_008 1239
12.4%
age_005 1215
12.2%
age_001 830
 
8.3%
age_006 519
 
5.2%
age_007 74
 
0.7%

씠슜嫄댁닔
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2488
Minimum1
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:24:15.320541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile6
Maximum26
Range25
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.0806565
Coefficient of variation (CV)0.92522968
Kurtosis14.252965
Mean2.2488
Median Absolute Deviation (MAD)0
Skewness3.0479368
Sum22488
Variance4.3291315
MonotonicityNot monotonic
2024-03-14T01:24:15.427541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1 5195
51.9%
2 2078
 
20.8%
3 1063
 
10.6%
4 576
 
5.8%
5 375
 
3.8%
6 256
 
2.6%
7 157
 
1.6%
8 88
 
0.9%
9 59
 
0.6%
10 46
 
0.5%
Other values (13) 107
 
1.1%
ValueCountFrequency (%)
1 5195
51.9%
2 2078
 
20.8%
3 1063
 
10.6%
4 576
 
5.8%
5 375
 
3.8%
6 256
 
2.6%
7 157
 
1.6%
8 88
 
0.9%
9 59
 
0.6%
10 46
 
0.5%
ValueCountFrequency (%)
26 1
 
< 0.1%
25 1
 
< 0.1%
22 2
 
< 0.1%
21 1
 
< 0.1%
19 1
 
< 0.1%
18 1
 
< 0.1%
17 5
0.1%
16 9
0.1%
15 6
0.1%
14 11
0.1%

슫룞
Text

Distinct6743
Distinct (%)67.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-14T01:24:15.741420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.1751
Min length2

Characters and Unicode

Total characters51751
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5334 ?
Unique (%)53.3%

Sample

1st row85.99
2nd row151.06
3rd row20.20
4th row232.34
5th row0.00
ValueCountFrequency (%)
0.00 1062
 
10.6%
n 41
 
0.4%
21.62 14
 
0.1%
36.55 11
 
0.1%
28.83 10
 
0.1%
38.61 9
 
0.1%
15.44 9
 
0.1%
37.07 9
 
0.1%
27.03 8
 
0.1%
19.82 8
 
0.1%
Other values (6733) 8819
88.2%
2024-03-14T01:24:16.147269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9959
19.2%
0 6403
12.4%
1 5678
11.0%
2 4795
9.3%
3 4167
8.1%
4 3868
 
7.5%
5 3601
 
7.0%
6 3477
 
6.7%
7 3376
 
6.5%
9 3221
 
6.2%
Other values (3) 3206
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 41710
80.6%
Other Punctuation 10000
 
19.3%
Uppercase Letter 41
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6403
15.4%
1 5678
13.6%
2 4795
11.5%
3 4167
10.0%
4 3868
9.3%
5 3601
8.6%
6 3477
8.3%
7 3376
8.1%
9 3221
7.7%
8 3124
7.5%
Other Punctuation
ValueCountFrequency (%)
. 9959
99.6%
\ 41
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
N 41
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 51710
99.9%
Latin 41
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9959
19.3%
0 6403
12.4%
1 5678
11.0%
2 4795
9.3%
3 4167
8.1%
4 3868
 
7.5%
5 3601
 
7.0%
6 3477
 
6.7%
7 3376
 
6.5%
9 3221
 
6.2%
Other values (2) 3165
 
6.1%
Latin
ValueCountFrequency (%)
N 41
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51751
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9959
19.2%
0 6403
12.4%
1 5678
11.0%
2 4795
9.3%
3 4167
8.1%
4 3868
 
7.5%
5 3601
 
7.0%
6 3477
 
6.7%
7 3376
 
6.5%
9 3221
 
6.2%
Other values (3) 3206
 
6.2%

깂냼
Text

Distinct536
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-14T01:24:16.478876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.9921
Min length2

Characters and Unicode

Total characters39921
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique136 ?
Unique (%)1.4%

Sample

1st row0.67
2nd row1.43
3rd row0.16
4th row1.87
5th row0.00
ValueCountFrequency (%)
0.00 1069
 
10.7%
0.27 134
 
1.3%
0.19 131
 
1.3%
0.32 120
 
1.2%
0.23 115
 
1.1%
0.17 112
 
1.1%
0.29 109
 
1.1%
0.16 108
 
1.1%
0.18 106
 
1.1%
0.13 106
 
1.1%
Other values (526) 7890
78.9%
2024-03-14T01:24:16.955395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 10904
27.3%
. 9959
24.9%
1 3824
 
9.6%
2 2953
 
7.4%
3 2320
 
5.8%
4 1943
 
4.9%
5 1748
 
4.4%
6 1711
 
4.3%
7 1605
 
4.0%
8 1456
 
3.6%
Other values (3) 1498
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29880
74.8%
Other Punctuation 10000
 
25.0%
Uppercase Letter 41
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10904
36.5%
1 3824
 
12.8%
2 2953
 
9.9%
3 2320
 
7.8%
4 1943
 
6.5%
5 1748
 
5.9%
6 1711
 
5.7%
7 1605
 
5.4%
8 1456
 
4.9%
9 1416
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 9959
99.6%
\ 41
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
N 41
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 39880
99.9%
Latin 41
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10904
27.3%
. 9959
25.0%
1 3824
 
9.6%
2 2953
 
7.4%
3 2320
 
5.8%
4 1943
 
4.9%
5 1748
 
4.4%
6 1711
 
4.3%
7 1605
 
4.0%
8 1456
 
3.7%
Other values (2) 1457
 
3.7%
Latin
ValueCountFrequency (%)
N 41
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39921
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10904
27.3%
. 9959
24.9%
1 3824
 
9.6%
2 2953
 
7.4%
3 2320
 
5.8%
4 1943
 
4.9%
5 1748
 
4.4%
6 1711
 
4.3%
7 1605
 
4.0%
8 1456
 
3.6%
Other values (3) 1498
 
3.8%

씠룞嫄곕━(M)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct5905
Distinct (%)59.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3610.5616
Minimum0
Maximum55875.85
Zeros1099
Zeros (%)11.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:24:17.072325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1930
median2130
Q34611.5225
95-th percentile12290.817
Maximum55875.85
Range55875.85
Interquartile range (IQR)3681.5225

Descriptive statistics

Standard deviation4456.8015
Coefficient of variation (CV)1.234379
Kurtosis13.749552
Mean3610.5616
Median Absolute Deviation (MAD)1490
Skewness2.9655431
Sum36105616
Variance19863079
MonotonicityNot monotonic
2024-03-14T01:24:17.174934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1099
 
11.0%
1190.0 22
 
0.2%
840.0 22
 
0.2%
1180.0 21
 
0.2%
940.0 20
 
0.2%
1420.0 19
 
0.2%
1150.0 19
 
0.2%
1250.0 18
 
0.2%
1160.0 18
 
0.2%
1470.0 18
 
0.2%
Other values (5895) 8724
87.2%
ValueCountFrequency (%)
0.0 1099
11.0%
0.1 4
 
< 0.1%
0.26 1
 
< 0.1%
10.0 5
 
0.1%
20.0 1
 
< 0.1%
60.0 2
 
< 0.1%
70.0 1
 
< 0.1%
88.13 2
 
< 0.1%
88.14 2
 
< 0.1%
88.15 1
 
< 0.1%
ValueCountFrequency (%)
55875.85 1
< 0.1%
48323.52 1
< 0.1%
45032.57 1
< 0.1%
42976.05 1
< 0.1%
42117.8 1
< 0.1%
41101.65 1
< 0.1%
38896.15 1
< 0.1%
38059.14 1
< 0.1%
37850.0 1
< 0.1%
35996.55 1
< 0.1%

씠슜떆媛(遺
Real number (ℝ)

HIGH CORRELATION 

Distinct309
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.5911
Minimum0
Maximum677
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:24:17.302938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q110
median22
Q351
95-th percentile137
Maximum677
Range677
Interquartile range (IQR)41

Descriptive statistics

Standard deviation50.626722
Coefficient of variation (CV)1.247237
Kurtosis17.704769
Mean40.5911
Median Absolute Deviation (MAD)15
Skewness3.240756
Sum405911
Variance2563.065
MonotonicityNot monotonic
2024-03-14T01:24:17.422550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 374
 
3.7%
4 332
 
3.3%
6 326
 
3.3%
7 318
 
3.2%
8 300
 
3.0%
9 297
 
3.0%
10 288
 
2.9%
3 275
 
2.8%
13 260
 
2.6%
11 241
 
2.4%
Other values (299) 6989
69.9%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 47
 
0.5%
2 149
 
1.5%
3 275
2.8%
4 332
3.3%
5 374
3.7%
6 326
3.3%
7 318
3.2%
8 300
3.0%
9 297
3.0%
ValueCountFrequency (%)
677 1
< 0.1%
627 2
< 0.1%
571 1
< 0.1%
537 1
< 0.1%
521 1
< 0.1%
518 1
< 0.1%
449 1
< 0.1%
426 1
< 0.1%
409 1
< 0.1%
408 1
< 0.1%

Interactions

2024-03-14T01:24:12.957754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:24:11.990368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:24:12.348416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:24:12.646453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:24:13.058871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:24:12.063135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:24:12.423555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:24:12.737368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:24:13.139513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:24:12.160057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:24:12.497620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:24:12.809440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:24:13.214925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:24:12.255230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:24:12.566902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:24:12.877220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T01:24:17.518443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호대여구분코드성별연령대코드씠슜嫄댁닔씠룞嫄곕━(M)씠슜떆媛(遺
대여소번호1.0000.0520.0400.0760.1010.0870.088
대여구분코드0.0521.0000.1880.4120.1660.0920.106
성별0.0400.1881.0000.1780.0850.0630.025
연령대코드0.0760.4120.1781.0000.1870.1060.094
씠슜嫄댁닔0.1010.1660.0850.1871.0000.7120.688
씠룞嫄곕━(M)0.0870.0920.0630.1060.7121.0000.685
씠슜떆媛(遺0.0880.1060.0250.0940.6880.6851.000
2024-03-14T01:24:17.616280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여구분코드성별연령대코드
대여구분코드1.0000.0750.194
성별0.0751.0000.080
연령대코드0.1940.0801.000
2024-03-14T01:24:17.691280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호씠슜嫄댁닔씠룞嫄곕━(M)씠슜떆媛(遺대여구분코드성별연령대코드
대여소번호1.000-0.022-0.044-0.0680.0310.0240.036
씠슜嫄댁닔-0.0221.0000.6050.6360.1000.0480.089
씠룞嫄곕━(M)-0.0440.6051.0000.7380.0550.0380.050
씠슜떆媛(遺-0.0680.6360.7381.0000.0630.0150.045
대여구분코드0.0310.1000.0550.0631.0000.0750.194
성별0.0240.0480.0380.0150.0751.0000.080
연령대코드0.0360.0890.0500.0450.1940.0801.000

Missing values

2024-03-14T01:24:13.334356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T01:24:13.468954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

대여일자대여소번호대여소대여구분코드성별연령대코드씠슜嫄댁닔슫룞깂냼씠룞嫄곕━(M)씠슜떆媛(遺
50992021-12-01543543. 구의공원(테크노마트 앞)정기\NAGE_005385.990.672895.3849
15542021-12-01229229. 양평1 보행육교 앞정기FAGE_0082151.061.436166.8170
45292021-12-01488488.푸르메병원정기\NAGE_008120.200.16680.04
74292021-12-01763763. 목동11단지 아파트정기\NAGE_0047232.341.878031.64124
47712021-12-01509509. 이마트 버스정류소 옆정기<NA>AGE_00410.000.000.07
40822021-12-01436436. 이대역 5번출구정기\NAGE_003162.000.451956.9941
54092021-12-01568568. 청계8가사거리 부근정기<NA>AGE_0041144.701.215220.041
60772021-12-01631631. 답십리역 1번출구정기\NAGE_005110.100.08340.03
105752021-12-0111181118. 증미역 3번출구뒤(등촌두산위브센티움오피스텔)정기MAGE_0083153.971.295527.0535
72482021-12-01747747. 목동3단지 상가정기\NAGE_00426.540.07284.7735
대여일자대여소번호대여소대여구분코드성별연령대코드씠슜嫄댁닔슫룞깂냼씠룞嫄곕━(M)씠슜떆媛(遺
68112021-12-01709709. 신정3동 현장민원실 앞정기\NAGE_00210.000.000.05
79722021-12-01796796.목동아파트 14단지 B상가 앞정기<NA>AGE_002236.550.301300.022
56032021-12-01585585. 성수2가1동 공영주차장 인근정기<NA>AGE_002213.330.16701.048
6842021-12-01156156. 서울서부지방법원 앞정기MAGE_00817.530.07283.812
18502021-12-01245245. 삼성생명 당산사옥 앞정기MAGE_008579.030.723070.057
20442021-12-01262262. 영문초등학교 사거리일일(회원)FAGE_002153.380.482073.6317
60892021-12-01631631. 답십리역 1번출구정기MAGE_0039219.961.797688.0766
44242021-12-01475475.DDP 패션몰정기FAGE_005154.990.532276.6215
49312021-12-01524524. 래미안금호하이리버 아파트 102동 옆정기FAGE_0021105.561.104760.028
64982021-12-01669669.청계한신휴플러스앞 삼거리일일(회원)MAGE_002142.750.391660.7214